class_docstr: An implementation of `"Label Propagation Clustering" <https://arxiv.org/abs/0709.2938>`_
  from the Physical Review '07 paper "Near Linear Time Algorithm to Detect Community Structures
  in Large-Scale Networks". The tool executes a series of label propagations with unique labels.
  The final labels are used as cluster memberships.
  
  Args:
      seed (int): Random seed. Default is 42.
      iterations (int): Propagation iterations. Default is 100.
code: def __init__(self, seed: int = 42, iterations: int = 100):
      self.seed = seed
      self.iterations = iterations
  
file_path: karateclub/karateclub/community_detection/non_overlapping/label_propagation.py
imports: import random
  import networkx
  from typing import Dict
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new LabelPropagation object from inherited base classes: Estimator with example: obj = LabelPropagation(seed, iterations)